Autocorrelated and Heteroscedastic Disturbances

Regression models with nonspherical errors, and HAC and FGLS estimators

To explicitly model for serial correlation in the disturbance series, create a regression model with ARIMA errors (regARIMA model object). Alternatively, to acknowledge the presence of nonsphericality, you can estimate a heteroscedastic-and-autocorrelation-consistent (HAC) coefficient covariance matrix, or implement feasible generalized least squares (FGLS). For more details on HAC and FGLS estimators, see Time Series Regression X: Generalized Least Squares and HAC Estimators.

Export variables to the MATLAB® Workspace, generate plain text and live functions that return a model estimated in an app session, or generate a report recording your activities on time series and estimated models in an Econometric Modeler app session.